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Can brain signals and anatomy refine contact choice for deep brain stimulation in Parkinson’s disease?
  1. San San Xu1,2,3,
  2. Wee-Lih Lee1,
  3. Thushara Perera1,3,
  4. Nicholas C Sinclair1,3,
  5. Kristian J Bulluss1,4,5,6,
  6. Hugh J McDermott1,3,
  7. Wesley Thevathasan1,2,7,8
  1. 1Bionics Institute, East Melbourne, Victoria, Australia
  2. 2Department of Neurology, Austin Hospital, Heidelberg, Victoria, Australia
  3. 3Medical Bionics Department, The University of Melbourne, Melbourne, Victoria, Australia
  4. 4Department of Neurosurgery, St Vincent's Hospital, Fitzroy, Victoria, Australia
  5. 5Department of Neurosurgery, Austin Hospital, Heidelberg, Victoria, Australia
  6. 6Department of Surgery, The University of Melbourne, Parkville, Victoria, Australia
  7. 7Department of Neurology, The Royal Melbourne Hospital, Parkville, Victoria, Australia
  8. 8Department of Medicine, The University of Melbourne, Parkville, Victoria, Australia
  1. Correspondence to Dr Wesley Thevathasan, Bionics Institute, East Melbourne, VIC 3050, Australia; Wesley.Thevathasan{at}mh.org.au

Abstract

Introduction Selecting the ideal contact to apply subthalamic nucleus deep brain stimulation (STN-DBS) in Parkinson’s disease is time-consuming and reliant on clinical expertise. The aim of this cohort study was to assess whether neuronal signals (beta oscillations and evoked resonant neural activity (ERNA)), and the anatomical location of electrodes, can predict the contacts selected by long-term, expert-clinician programming of STN-DBS.

Methods We evaluated 92 hemispheres of 47 patients with Parkinson’s disease receiving chronic monopolar and bipolar STN-DBS. At each contact, beta oscillations and ERNA were recorded intraoperatively, and anatomical locations were assessed. How these factors, alone and in combination, predicted the contacts clinically selected for chronic deep brain stimulation at 6 months postoperatively was evaluated using a simple-ranking method and machine learning algorithms.

Results The probability that each factor individually predicted the clinician-chosen contact was as follows: ERNA 80%, anatomy 67%, beta oscillations 50%. ERNA performed significantly better than anatomy and beta oscillations. Combining neuronal signal and anatomical data did not improve predictive performance.

Conclusion This work supports the development of probability-based algorithms using neuronal signals and anatomical data to assist programming of deep brain stimulation.

  • EVOKED POTENTIALS
  • PARKINSON'S DISEASE
  • ELECTRICAL STIMULATION
  • NEUROSURGERY
  • NEUROPHYSIOLOGY

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Footnotes

  • Contributors SSX was involved with the conception, organisation and execution of the research project, the statistical analysis and the writing of the first draft of the manuscript. W-LL was involved in the organisation and execution of the research project and the statistical analysis and drafting, review and critique of the manuscript. TP was involved in the organisation and execution of the research project and review and critique of the manuscript. NCS was involved in the conception, organisation and execution of the research project and the review and critique of the manuscript. KJB was involved in the organisation and execution of the research project and the review and critique of the manuscript. HJM was involved in the organisation and execution of the research project and the review and critique of the manuscript. WT was involved in the conception, organisation and execution of the research project and the review and critique of the manuscript.

  • Funding This study was funded through the National Health and Medical Research Council (Development grant number 1177815, project grant number 1 103 238 (Bionics Institute), post graduate scholarship number 1 133 295 (SS.X)), St Vincent’s Hospital Research Endowment Fund and Colonial Foundation. WT is also supported through Lions International. All authors affiliated with the Bionics Institute acknowledge the support it receives from the Victorian Government through its operational infrastructure programme.

  • Competing interests SSX holds options in DBS Technologies Pty Ltd. W-LL has no relevant financial disclosures. TP receives consulting fees and holds options in DBS Technologies Pty Ltd and is a named inventor on related patents, which are assigned to DBS Technologies Pty Ltd. NCS is a named inventor on related patents, which are assigned to DBS Technologies Pty Ltd. KJB, HJM and WT are co-founders and hold shares and options in DBS Technologies Pty Ltd which plans to commercialise the use of neuronal signals to improve DBS. KJB, HJM and WT are also named inventors on related patents, which are assigned to DBS Technologies Pty Ltd.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.